Electronic apparatus and control method thereof

ABSTRACT

An electronic apparatus and a control method thereof are provided. The electronic apparatus may include an interface; and a processor configured to obtain, via the interface, information related to values, which occur in time series, of a plurality of factors regarding a prediction object, identify, based on the information related to the values of the plurality of factors, at least one factor, from among the plurality of factors, having a time series change of values that corresponds to a time series change of reference values of the prediction object, and output information related to a predicted value of the prediction object based on the time series change of the values of the at least one factor.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. § 119to Korean Patent Application No. 10-2020-0063406, filed on May 27, 2020,in the Korean Intellectual Property Office, the disclosure of which isincorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to an electronic apparatus and a control methodthereof which predicts and calculates a value of an object to bepredicted.

2. Description of Related Art

Although a simple linear regression of factors which have a rationalcausal relationship can be used to predict a value of an object to bepredicted, it becomes more important to select a significant factor as atype and volume of data become larger.

As a method of selecting the factor, for example, a forward selection,which selects a factor of high significance using linear relationbetween the factors, a backward elimination, which eliminates a factorof low significance, etc., are used. Another method is used to make aset of factors which have no multicollinearity by classifying aplurality of principal components using variance or covariance relationbetween the factors and forming new factors through a linear combinationof the factors which are included in the classification of eachprincipal component.

However, because such methods of selecting the factors have referencesonly to the value itself based on a difference between a predicted valueand an actual value, a ratio, etc., and calculate only a relation indexbetween individual factors and corresponding values, there is a problemwhere an interaction effect between the individual factors which are notconsidered cannot be applied. Also, there is another problem whereexplanatory power decreases as a factor which has no relation with theobject to be predicted is selected.

SUMMARY

Provided are an electronic apparatus and a control method thereof whichmore accurately predict a value of an object to be predicted.

According to an aspect of the disclosure, an electronic apparatus mayinclude an interface; and a processor configured to obtain, via theinterface, information related to values, which occur in time series, ofa plurality of factors regarding a prediction object, identify, based onthe information related to the values of the plurality of factors, atleast one factor, from among the plurality of factors, having a timeseries change of values that corresponds to a time series change ofreference values of the prediction object, and output informationrelated to a predicted value of the prediction object based on the timeseries change of the values of the at least one factor.

The processor may identify a point at which the values of the pluralityof factors change in time series from increasing to decreasing or changein time series from decreasing to increasing, and identify the at leastone factor based on the point.

The point may be a first point, and the processor may identify the atleast one factor based on a relation between a number of first pointsand a number of second points at which the reference values change intime series from increasing to decreasing or change in time series fromdecreasing to increasing.

The processor may determine a probability of each of the plurality offactors according to a precedence degree and an accuracy which arepredicted from the values of the plurality of factors, and determine aprediction validity of each of the plurality of factors based on thecorresponding probability.

The processor may identify an increase or a decrease of the time serieschange of the values of the plurality of factors based on theinformation related to the values of the plurality of factors, andidentify the at least one factor based on the increase or the decreaseof the time series change.

The processor may group the plurality of factors into a plurality ofgroups which are different from one another based on a relation to theprediction object, and identify the at least one factor based on changesof values of the plurality of factors which are included in theplurality of groups.

The processor may determine coordinates for each of the plurality offactors according to the relation, and group factors, from among theplurality of factors, having similar coordinates into a same group.

The processor may identify the at least one factor based on values offactors which have representativeness in each of the plurality ofgroups.

The processor may identify whether a time series change of values of acombination of factors of the plurality of factors, which are selectedfrom the plurality of groups, corresponds to the time series change ofthe reference values.

According to an aspect of the disclosure, a method of controlling anelectronic apparatus may include obtaining, via an interface,information related to values, which occur in time series, of aplurality of factors regarding a prediction object, identifying, basedon the information related to the values of the plurality of factors, atleast one factor, from among the plurality of factors, having a timeseries change of values that corresponds to a time series change ofreference values of the prediction object, and outputting informationrelated to a predicted value of the prediction object based on the timeseries change of the values of the at least one factor.

The method may include identifying a point at which the values of theplurality of factors change in time series from increasing to decreasingor change in time series from decreasing to increasing, and identifyingthe at least one factor based on the point.

The point is a first point, and the identifying the at least one factormay include identifying the at least one factor based on a relationbetween a number of first points and a number of second points at whichthe reference values change in time series from increasing to decreasingor change in time series from decreasing to increasing.

The identifying the at least one factor may include determining aprobability of each of the plurality of factors according to aprecedence degree and an accuracy which are predicted from the values ofthe plurality of factors, and determining a prediction validity of eachof the plurality of factors based on the corresponding probability.

The identifying the at least one factor may include identifying anincrease or a decrease of the time series change of the values of theplurality of factors based on the information related to the values ofthe plurality of factors, and identifying the at least one factor basedon the increase or the decrease of the time series change.

The identifying the at least one factor may include grouping theplurality of factors into a plurality of groups which are different fromone another based on a relation to the prediction object, andidentifying the at least one factor based on changes of values of theplurality of factors which are included in the plurality of groups.

The grouping the plurality of factors into the plurality of groups mayinclude determining coordinates for each of the plurality of factorsaccording to the relation, and grouping factors, from among theplurality of factors, having similar coordinates into a same group.

The identifying the at least one factor may include identifying the atleast one factor based on values of factors which haverepresentativeness in each of the plurality of groups.

The identifying the at least one factor may include identifying whethera time series change of values of a combination factors of the pluralityof factors, which are selected from the plurality of groups, correspondsto the time series change of the reference values.

A non-transitory computer-readable medium may store a computer programincluding computer-readable code to perform a method of controlling anelectronic apparatus. The method may include obtaining, via aninterface, information related to values, which occur in time series, ofa plurality of factors regarding a prediction object, identifying, basedon the information related to the values of the plurality of factors, atleast one factor, from among the plurality of factors, having a timeseries change of values that corresponds to a time series change ofreference values of the prediction object, and outputting informationrelated to a predicted value of the prediction object based on the timeseries change of the values of the at least one factor.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the present disclosure will be more apparent from thefollowing description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 illustrates an electronic apparatus according to an exampleembodiment;

FIG. 2 is a block diagram illustrating configurations of the electronicapparatus according to an example embodiment;

FIG. 3 is a flowchart illustrating operations of the electronicapparatus according to an example embodiment;

FIG. 4 illustrates a flow of operations of the electronic apparatusaccording to an example embodiment;

FIG. 5 illustrates a graph in which a plurality of factors are groupedaccording to an example embodiment;

FIG. 6 illustrates graphs which include time series prediction accordingto an example embodiment;

FIG. 7 illustrates a flowchart of the electronic apparatus based on theup/down index according to an example embodiment;

FIG. 8 illustrates a flowchart of the electronic apparatus based on theextremum index according to an example embodiment;

FIG. 9 illustrates a flowchart of the electronic apparatus based on theprecedence prediction index according to an example embodiment; and

FIG. 10 illustrates a table used for the precedence prediction index.

DETAILED DESCRIPTION

Hereinafter, embodiments of the disclosure will be described in detailwith reference to the accompanying drawings. In the drawings, the samereference numbers or signs refer to components that performsubstantially the same function, and the size of each component in thedrawings may be exaggerated for clarity and convenience. However, thetechnical idea and the core configuration and operation of thedisclosure are not limited only to the configuration or operationdescribed in the following examples. In describing the disclosure, if itis determined that a detailed description of the known technology orconfiguration related to the disclosure may unnecessarily obscure thesubject matter of the disclosure, the detailed description thereof willbe omitted.

In the description of the embodiments, terms including ordinal numberssuch as “first” and “second” are used only for the purpose ofdistinguishing one component from other components, and singularexpressions include plural expressions unless the context clearlyindicates otherwise. Also, in the description of the embodiments, itshould be understood that terms such as “configured,” “include,” and“have” do not preclude the existence or addition possibility of one ormore other features or numbers, steps, operations, components, parts, orcombinations thereof. In addition, in the description of theembodiments, a “module” or a “unit” performs at least one function oroperation, and may be implemented in hardware or software, or acombination of hardware and software, and may be integrated into atleast one module. In addition, in the description of the embodiments, atleast one of the plurality of elements refers to not only all of theplurality of elements, but also each one or all combinations thereofexcluding the rest of the plurality of elements.

FIG. 1 illustrates an electronic apparatus according to an exampleembodiment.

The electronic apparatus 100 which outputs information on a predictedvalue of a prediction object according to an example embodiment may beembodied in various ways. Accordingly, as illustrated in FIG. 1, theelectronic apparatus 100 may be embodied as, for example, a computer, aserver, a display apparatus, etc. Also, the electronic apparatus 100 maybe embodied as a recording medium which stores a computer programincluding computer-readable code to perform a method of controlling theelectronic apparatus.

The electronic apparatus 100 obtains information on a plurality offactors regarding a prediction object, and, through a process, outputsinformation on a predicted value based on the obtained information. Theprediction object may be an object for which a value or figure is to bepredicted such as, for example, a price of a panel of a displayapparatus. The plurality of factors regarding the prediction object maybe factors which relate to the prediction object and provides data as abasis of calculating a value of the prediction object such as, forexample, a past material price, a stock price, an economic indicator, ademand for the panel, etc.

The electronic apparatus 100 according to an embodiment does not onlyconsider an error between the predicted value and a reference valuewhich is an actual value, but also selects a combination of optimalfactors based on values, which occur in time series, of the plurality offactors. Here, a trend of change of the values may be more importantthan a trend of the values hereafter in prediction using time serieschange of the factors. Therefore, in order to detect a pattern of thechange, it is possible to perform prediction based on an index whichemphasizes a necessary (or important) feature of the object to bepredicted from the combined factors. Such prediction may minimize anabsolute error between the predicted value and the reference value andallow the predicted value to be achieved in accordance with the patternof actual time series, which may be substantially used in making adecision afterwards based the prediction.

Therefore, in selecting the factor and a model regarding the predictionobject to calculate the predicted value of the prediction object moreaccurately, a solution that the electronic apparatus 100 according to anembodiment uses a prediction index which is suitable for a necessary (orimportant) condition of prediction results and a time series model willbe suggested below.

FIG. 2 is a block diagram illustrating configurations of the electronicapparatus according to an embodiment.

As illustrated in FIG. 2, the electronic apparatus 100 includes aninterface 110. The electronic apparatus 100 obtains, through theinterface 110, information on or related to values, which occurred inthe past in time series at a plurality of points, of the plurality offactors regarding the prediction object.

The interface 110 includes a wired interface 111. The wired interface111 may include a connector or port to which an antenna for receiving abroadcast signal based on broadcasting standards forterrestrial/satellite broadcasting, etc., is connected or to which acable for receiving a broadcast signal based on cable broadcastingstandards is connected. Alternatively, the electronic apparatus 100 mayinclude a built-in antenna to receive a broadcast signal. The wiredinterface 111 may include a connector, a port, etc., such as a highdefinition multimedia interface (HDMI) port, a DisplayPort, a DVI port,and the like, based on video and/or audio transmission standards such asthunderbolt, composite video, component video, super video, Syndicat desConstructeurs d'Appareils Radiorécepteurs et Téléviseurs (SCART), etc.The wired interface 111 may include a connector, port, etc., based onuniversal data transmission standards such as a universal serial bus(USB) port. The wired interface 111 may include a connector, a port,etc., to which an optical cable based on optical transmission standardsis connected. The wired interface 111 may include a connector, a port,etc., which connects with an external microphone or an external audiodevice including a microphone, and receives an audio signal from theaudio device. The wired interface 111 may include a connector, a port,etc., which connects with an audio device such as a headset, anearphone, an external loudspeaker, etc., and transmits or outputs anaudio signal to the audio device. The wired interface 111 may include aconnector, a port, etc., based on Ethernet, or the like, networktransmission standards. For example, the wired interface 111 may beembodied by a local area network (LAN) card, or the like, connected to arouter or gateway by a wire.

The wired interface 111 may be connected to a set-top box, an opticalmedia player, a loudspeaker, a server, etc., in a manner of 1:1 or 1:N(where N is a natural number) through the foregoing connectors, ports,etc., by a wire, thereby receiving a video/audio signal from theconnected external device or transmitting a video/audio signal to theconnected external device. The wired interface 111 may includeconnectors, ports, etc., to transmit the video/audio signalsindividually.

Further, according to this example embodiment, the wired interface 111may be internally provided in the electronic apparatus 100, or may bedetachably connected to the connector of the electronic apparatus 100 asprovided in the form of a dongle or module.

The interface 110 may include a wireless interface 112. The wirelessinterface 112 may be variously embodied corresponding to the electronicapparatus 100. For example, the wireless interface 112 may use wirelesscommunication methods such as radio frequency (RF), Zigbee, Bluetooth,wireless fidelity (Wi-Fi), Wi-Fi Direct (WFD), ultra wideband (UWB),near field communication (NFC), etc. The wireless interface 112 may beembodied by a wireless communication module based on Wi-Fi, Bluetooth,or the like, for one-to-one direct wireless communication. The wirelessinterface 112 performs wireless communication with a server via anetwork, thereby exchanging a data packet with the server. The wirelessinterface 112 may include an infrared (IR) transmitter and/or an IRreceiver to transmit and/or receive an IR signal based on IRcommunication standards. Through the IR transmitter and/or the IRreceiver, the wireless interface 112 may receive or input therein aremote control signal from a remote controller or another externaldevice, or transmit or output the remote control signal to anotherexternal device. Alternatively, the electronic apparatus 100 mayexchange the remote control signal with the remote controller or otherexternal devices through the wireless interface 112 of differentstandards such as Wi-Fi, Bluetooth, etc.

When the video/audio signal received through the interface 110 is abroadcast signal, the electronic apparatus 100 may further include atuner to be tuned to a channel for the received broadcast signal.

The electronic apparatus 100 may include a display 120. The display 120includes a display panel configured to display an image on a screen. Thedisplay panel is provided to have a light receiving structure such as aliquid crystal type, or a self-emissive structure such as an organiclight emitting diode (OLED) type. The display 120 may include anadditional element according to the structure of the display panel. Forexample, when the display panel is the liquid crystal type, the display120 includes a liquid crystal display panel, a backlight unit configuredto emit light, and a panel driving substrate configured to drive liquidcrystal of the liquid crystal display panel.

The electronic apparatus 100 may include a user interface 130. The userinterface 130 includes circuitry related to various input interfacesprovided to receive a user input. The user interface 130 may bevariously configured according to the kinds of the electronic apparatus100, and may, for example, include a mechanical or electronic button ofthe electronic apparatus 100, a remote controller separated from theelectronic apparatus 100, an input interface provided in an externaldevice connected to the electronic apparatus 100, a touch pad, a touchscreen installed in the display 120, etc.

The electronic apparatus 100 may include a storage 140. The storage 140is configured to store digitalized data. The storage 140 includes anonvolatile storage in which data is retained regardless of whetherpower is on or off, and a volatile memory into which data to beprocessed by a processor 180 is loaded and in which data is retainedonly when power is on. The storage 140 includes a flash memory, ahard-disc drive (HDD), a solid-state drive (SSD), a read only memory(ROM), etc., and the memory includes a buffer, a random-access memory(RAM), etc.

The electronic apparatus 100 may include a microphone 150. Themicrophone 150 collects a voice of a user, and the like, sounds fromexternal environments, etc. The microphone 150 transmits a signal basedon a collected sound to the processor 180. The electronic apparatus 100may include the microphone 150 for collecting a voice of a user, orreceiving an audio signal from an external apparatus having a microphonesuch as the remote controller, the smartphone, or the like, through theinterface 110. The external apparatus may be installed with aremote-control application to control the electronic apparatus 100 orperform voice recognition or a similar function. With such an installedapplication, the external apparatus may receive a voice of a user,exchange data and control with the electronic apparatus 100 throughWi-Fi, Bluetooth, IR, etc., where a plurality of interfaces 110corresponding to the communication methods may be present in theelectronic apparatus 100.

The electronic apparatus 100 may include a speaker 160. The speaker 160outputs a sound based on audio data processed by the processor 180. Thespeaker 160 includes a unit speaker provided corresponding to audio dataof a certain audio channel, and thus may include a plurality of unitspeakers respectively corresponding to the audio data of the pluralityof audio channels. According to another example embodiment, the speaker160 may be provided separately from the electronic apparatus 100, andthe electronic apparatus 100 in this case may transmit the audio data tothe speaker 160 through the interface 110.

The electronic apparatus 100 may include a sensor 170. The sensor 170senses a state of the electronic apparatus 100 and forwards informationon the sensed state to the processor 180. The sensor 170 includes atleast one of a magnetic sensor, an acceleration sensor, atemperature/humidity sensor, an infrared sensor, a gyro sensor, aposition sensor such as a GPS sensor, an atmospheric pressure sensor, aproximity sensor, an illuminance sensor such as an RGB sensor, etc., butis not limited thereto. The processor 180 may store a sensing value inthe storage 140. Based on a user event being detected, the processor 180may identify whether the user event occurs based on whether a detectedsensing value corresponds to the stored sensing value.

The electronic apparatus 100 includes the processor 180. The processor180 includes one or more hardware processors embodied as a centralprocessing unit (CPU), a chipset, a buffer, a circuit, etc., which aremounted onto a printed circuit board, and may be embodied as a system onchip (SoC). When the electronic apparatus 100 is embodied as a displayapparatus, the processor 180 includes modules corresponding to variousprocesses, such as a demultiplexer, a decoder, a scaler, an audiodigital signal processor (DSP), an amplifier, etc. Here, some or all ofsuch modules may be embodied as an SOC. For example, video processingmodules such as the demultiplexer, the decoder, the scaler, and thelike, may be embodied as a video processing SOC, and the audio DSP maybe embodied as a chipset separately from the SOC.

The processor 180 may convert an audio signal into audio data based onthe audio signal related to a voice of a user being obtained via themicrophone 150, or the like. In this case, the audio data may includetext data obtained by a speech-to-text (STT) process that converts anaudio signal into text data. The processor 180 identifies a commandissued by the audio data, and performs an operation based on theidentified command. Both the process for the audio data and the processfor the command may be performed in the electronic apparatus 100.Alternatively, at least some processes may be performed by at least oneserver connected to and communicating with the electronic apparatus 100via a network, thereby conserving processor and memory resources of theelectronic apparatus 100.

The processor 180 may call and execute at least one instruction amongone or more software instructions stored in a computer-readable storagemedium which is readable by the electronic apparatus 100, or the like.This enables the electronic apparatus 100 to operate and perform atleast one function based on the at least one called instruction. The oneor more instructions may include code produced by a compiler or a codeexecutable by an interpreter. The computer-readable storage medium maybe provided in the form of a non-transitory storage medium. Here,“non-transitory”refers to the storage medium being a tangible device,and the term does not distinguish between cases of code or data beingsemi-permanently and temporarily stored in the storage.

The processor 180 may use at least one of a machine learning, neuralnetwork, or deep learning algorithm as a rule-base or artificialintelligence (AI) algorithm to perform at least a part of data analysis,processing, or result information generation for obtaining, via theinterface 110, information related to values, which occur in timeseries, of the plurality of factors regarding the prediction object,identifying at least one factor from among the plurality of factors,where time series change of the values of the identified at least onefactor corresponds to time series change of reference values which areprovided for the prediction object, and outputting information on apredicted value of the prediction object based on the values of theidentified at least one factor.

The processor 180 may perform a preprocess and a conversion of theplurality of factors or the values of the plurality of factors occurringin time series to have a form which is suitable for an input to an AImodel. The AI model may be achieved through learning. Here, theachievement through learning means that the AI model, that is, apredefined operation rule which is set to perform a feature or purposeto be desired is achieved by allowing a basic AI model to learn using aplurality of pieces of learning data through a learning algorithm. TheAI model may include a plurality of neural networks. Each layer of theplurality of neural networks has a plurality of weight values andperforms a neural network arithmetic operation through an operationbetween an operation result of a previous layer and the plurality ofweight values.

Inference prediction is a technique to infer logically and predict byjudging information and includes knowledge/probability-based reasoning,optimization prediction, preference-based planning, recommendation, etc.

For example, the processor 180 may function as both a learner and arecognizer. The learner may perform a function of generating the learnedneural network, and the recognizer may perform a function of recognizing(or inferring, predicting, estimating, and identifying) the data basedon the learned neural network. The learner may generate or update theneural network. The learner may obtain learning data to generate theneural network. For example, the learner may obtain the learning datafrom the storage 140 or from an external source. The learning data maybe data used for learning the neural network, and the data subjected tothe foregoing operations may be used as the learning data to teach theneural network.

Before teaching the neural network based on the learning data, thelearner may perform a preprocessing operation with regard to theobtained learning data or select data to be used in learning among aplurality of pieces of the learning data. For example, the learner mayprocess the learning data to have a preset format, apply filtering tothe learning data, or process the learning data to be suitable for thelearning by adding/removing noise to/from the learning data. The learnermay use the preprocessed learning data for generating the neural networkset to perform the operations.

The learned neural network may include a plurality of neural networks(or layers). The nodes of the plurality of neural networks have weights,and the plurality of neural networks may be connected to one another sothat an output value of a certain neural network can be used as an inputvalue of another neural network. The neural network may be aconvolutional neural network (CNN), a deep neural network (DNN), arecurrent neural network (RNN), a restricted Boltzmann machine (RBM), adeep belief network (DBN), a bidirectional recurrent deep neural network(BRDNN), deep Q-networks, or the like.

The recognizer may obtain target data to perform the foregoingoperations. The target data may be obtained from the storage 140 or froman external source. The target data may be data targeted for recognitionof the neural network. Before applying the target data to the learnedneural network, the recognizer may preprocess the obtained target dataor select data to be used in the recognition among a plurality of piecesof target data. For example, the recognizer may process the target datato have a preset format, apply filtering to the target data, oradd/remove noise to/from the target data, thereby processing the targetdata into data suitable for recognition. The recognizer applies thepreprocessed target data to the neural network, thereby obtaining anoutput value output from the neural network. The recognizer may obtain aprobability value or a reliability value together with the output value.

The control method of the electronic apparatus 100 may be provided in acomputer program product. The computer program product may includesoftware instructions to be executed by the processor 180 as describedabove. The computer program product may be traded as a commodity betweena seller and a buyer. The computer program product may be distributed inthe form of a computer-readable storage medium (e.g., a compact discread only memory (CD-ROM)) or may be directly distributed or distributedonline (e.g., downloaded or uploaded) between two user apparatuses(e.g., smartphones) via an application store (e.g., Play Store™). In thecase of online distribution, at least a part of the computer programproduct may be stored or produced in a computer-readable storage mediumsuch as a memory of a manufacturer server, an application-store server,or a relay server.

FIG. 3 is a flowchart illustrating operations of the electronicapparatus 100 according to an example embodiment.

According to an example embodiment, the processor 180 obtains, via theinterface 110, information related to values, which occur in timeseries, of the plurality of factors regarding the prediction object(operation S310). The processor 180 may receive data via the interface110 or use data stored in the storage 140 to extract a new factor whichrelates to the prediction object. The data may be provided in advance ormay be updated periodically. Therefore, the processor 180 extracts theplurality of factors from the data relating to the prediction object,and obtains the information related to the values of the extractedplurality of factors occurring in time series. The operation ofextracting the plurality of factors will be described in detailelsewhere herein.

The processor 180 identifies at least one factor, among the plurality offactors, where time series change of the values of the identified atleast one factor corresponds to time series change of reference valueswhich are provided for the prediction object (operation S320).

According to an embodiment, when extracting the plurality of factorsrelating to the prediction object for calculating the values of theprediction object as described above, the processor 180 may alsoidentify an optimal factor whose values corresponding to the referencevalues can be calculated accurately. The processor 180 may operate basedon an accuracy index which is set for operating in consideration of atime series feature of every predicted value as well as accuracy of thevalues in a prediction interval. That is, the processor 180 may identifythe at least one factor, among the plurality of factors, where the timeseries change of the values of the identified at least one factorcorresponds to the time series change of the reference values which areprovided for the prediction object. The reference values may be actualvalues which occurred at a past point for the prediction object.

The processor 180 may, using the accuracy index, identify whether thetime series change of the values of each factor corresponds to the timeseries change of the reference values. The accuracy index used in theoperation of the processor 180 of the example embodiment may be, forexample, an up/down index, an extremum index, a precedence predictionindex, etc. The up/down index uses increase or decrease of change in thevalues of the factor, whereas the extremum index uses an extremum, thatis, a point at which the values that are represented by the obtainedinformation change in time series from increase to decrease or viceversa. In the case of the precedence prediction index, a differencebetween an actual point and a point which is predicted at a timepreceding the predicted point is used. Each index will be described indetail elsewhere herein. The index may not only be a conventional index,but may also be set to include an item which is preferred by a user,which is not limited thereto.

The processor 180 outputs information related to the predicted value ofthe prediction object based on the values of the identified at least onefactor (operation S330).

The processor 180 may identify the at least one factor which correspondsto the time series change of the reference values based on each of theindexes, and output the information related to the predicted value ofthe prediction object based on the values of the identified factor.

According to an embodiment, because the time series change is used tocalculate the value of the prediction object, it is possible to preventthe prediction of a result which is different from an actual value dueto selection of a non-optimal factor, and to reduce a cost by finding acombination of optimal factors.

FIG. 4 illustrates a flow of operations of the electronic apparatus 100according to an embodiment. FIG. 4 illustrates the flowchart of FIG. 3more specifically, but does not limit the embodiment of FIG. 3.

The processor 180 obtains and inputs data to extract the factor relatingto the prediction object (operation S410). As described in operationS310 of FIG. 310, the processor 180 may receive data via the interface110 or use data which stored in the storage 140. The processor 180analyzes the obtained data based on the prediction object (operationS411). The process of analyzing the obtained data may be used for a datapre-process which includes grouping the related factors, may be used todetermine the accuracy index in modeling, or may be used to determine apriority in checking validity.

The processor 180 selects the plurality of factors throughpre-processing the obtained data (operation S420). The processor 180 maygenerate a factor pool which includes the factors used for a model aheadof modeling to calculate the predicted value of the prediction object.In order to generate the factor pool, first, the data pre-process isperformed on all the input data. Next, the feature of the predictionobject is checked through an analysis of the prediction object, and agroup or cluster is composed mainly of the prediction object.

That is, the data pre-processing includes grouping or clustering of thefactors (operation S421). The processor 180 may group the plurality offactors into a plurality of groups which are different from one anotherbased on a relation to the prediction object (operation S421). As amethod of grouping, for example, similar to FIG. 5, the processor 180gives the plurality of factors coordinates according to the relation,and groups each of the factors whose given coordinates are similar. Theprocessor 180 may generate the group from the data and the extractedfactors based on a prediction object-oriented keyword.

The pre-processing of time series data unlimitedly includes a techniqueto process the time series data suitably for a usable purpose. Thetechnique may be data extraction and standardization to make differenttypes of data into a uniform type of data usable for a prediction model,conversion for unit concordance among data such as time, amount, etc., astatistic technique regarding a representative value of data, factormodification and enhancement on pre-processed data, etc. Specifically,the technique may be converting, in the case of having to use monthlydata, quarterly data into the monthly data through linear interpolation,obtaining the factor by generating a representative value throughaveraging values whose sources are different, factor modification andenhancement such as generating a margin factor with a price and cost orgenerating an oversupply index or glut ratio with data of demand,supply, stock, etc. Such data pre-processing may be provided by a modelor program which is prepared to learn in advance.

The processor 180 generates a main factor pool from the generatedplurality of groups in consideration of the relation to the predictionobject, and representativeness of each of the groups (operation S430).As a result of the grouping, factors which have a high probability to beselected as the factor pool are the factors which highly relate to theprediction object and have high representativeness in each group. Theprocess of grouping is able to prevent multicollinearity by securing thefactors which relate to the prediction object while reducing correlationbetween other factors. According to this, the explanatory power of afinal model can be enforced.

In the case of using a model in an actual domain, a requisite factor isselected for the explanatory power of the model. The requisite factormay be included when composing various factor combinations forprediction, whereas any combination with a factor which is not requisitemay also be considered.

The processor 180 generates a model which uses the accuracy index toselect the optimal factor from the factor pool which is previouslygenerated by pre-processing the data (operation S440). As describedabove, the accuracy index may be an up/down index, an extremum index,and an precedence prediction index. The three indexes will be describedwith reference to FIGS. 7 through 10.

The processor 180 checks the validity of the predicted value which iscalculated using the generated model (operation S450). In thisoperation, the processor 180 does not only compare the indexes, but alsodefines and evaluates a normal category of the time series predictedvalues in a predicted interval based on a past pattern of the predictionobject. The normal category of the prediction object may include anupper/lower limit, a changing range, a changing frequency, etc.

For example, in the case of prediction based on the extremum index, anupper limit of a number of extrema which may occur in the predictedinterval is set. The processor 180 may not select a factor combinationwhere the number of extrema of the predicted values exceeds the setupper limit.

In order to respond flexibly in modeling the time series values havingvarious features, priority how to apply the accuracy indexes and thevalidity check changes variably based on feature analysis of theprediction object. For example, when there is a trend but a change issmall, the processor 180 may prioritize the extremum index and thevalidity check associated with the change because the change affects asa risk element. In contrast, when the change is large, the processor 180may prioritize the up/down index because of a seasonal or periodicchange.

The processor 180 may perform the operations of S440 and S450 withrespect to all of the factor combinations which can be generated in thefactor pool, and finally perform prediction based on a model which usesa highest index satisfying the prediction validity.

The processor 180 checks the validity, selects an optimal factorcombination from among the factor combinations in a basis of theplurality of models, and updates data (operation S460).

The processor 180 measures a degree of the prediction validity of apredicted result value regarding a factor-and-machine learning modelcombination which show a high time series index value for a validationdata set among predicted models which have learned based on learningdata sets. The processor 180 generates a final predicted model byselecting a factor-and-machine learning model combination whichsatisfies the prediction validity most. Here, if there is nofactor-and-machine learning model combination which satisfies theprediction validity, the processor 180 may generate at least onepredicted model by gradually alleviating a condition of the predictionvalidity, supplementing factor data, etc.

The processor 180 outputs information on the predicted value of theprediction object based on a value of the selected optimal factorcombination (operation S470).

According to an embodiment, it is possible to calculate and provide avalidated predicted value in which the time series change of theprediction object is considered. According to an embodiment, it is alsopossible to calculate a more accurate predicted value by setting theindex adaptively in accordance with the prediction object and modelingbased on the factors highly relating to the prediction object.

FIG. 5 illustrates a graph in which a plurality of factors are groupedaccording to an embodiment.

The graph 500 shows that, in the operation S421 of FIG. 4, the pluralityof factors are grouped into a plurality of groups which are differentfrom one another based on the relation to the prediction object.

The processor 180 groups the plurality of factors into the plurality ofgroups which are different from one another based on the relation to theprediction object, and identifies a factor which corresponds to a changeof the reference values based on a change of the factors which areincluded in each of the groups.

More specifically, first, the processor 180 searches a related factorbased on a prediction object-oriented keyword among data which can beobtained. For example, when the prediction object is a panel price,words such as “panel,” “price,” “demand of panel,” etc., may besearched.

Next, the processor 180 gives the plurality of factors, which areobtained by the search, a coordinate in accordance with the relationlike the graph 500, and groups each of the factors whose givencoordinates are similar. For example, the factors obtained by the searchword, whose relation is high, such as “demand of 24-inch panel,” “demandof 32-inch panel,” “demand of 40-inch panel,” etc., are given a similarcoordinate, and have high probability to be within a same group whenbeing grouped.

Accordingly, as illustrated in FIG. 5, the factors a1, a2, a3, and a4whose coordinates are similar are grouped as group A, the factors b1,b2, b3 and b4 are grouped as group B, and the factors c1, c2, c3 and c4are grouped as group C.

The processor 180 identifies a factor which corresponds to the change ofthe reference values based on a value of the factor which hasrepresentativeness in each of the groups. The process of grouping isable to prevent multicollinearity by securing the factors which relateto the prediction object while reducing correlation between otherfactors. For example, if the factors a1, a2, a3, and a4 in the group Aare all used to predict a value, the accuracy of prediction may beaffected because of high correlation between the factors. Therefore, itis possible to determine whether the relation between the factorsincluded in the group A is high and select the factor a1, which hasrepresentativeness among the group, as a representative factor.Described using the above example, because “demand of 24-inch panel,”“demand of 32-inch panel,” “demand of 40-inch panel,” etc., which isincluded in a same group, has a high probability to affect “price ofpanel” prediction redundantly, which affects the accuracy of theprediction, a factor having the representativeness may be set as therepresentative factor. In this way, it is possible to enforce theexplanatory power of the final model.

The processor 180 identifies whether a change of the values, which arein accordance with the combination of the plurality of factors selectedfrom the plurality of group, corresponds to a change of the referencevalues. The factors which are far away from each other in the graph 500,that is, whose relation is low, may be combined. This has a same purposeas selecting the representative factor as described above. When thefactors which are far away from each other are combined, the probabilityto select factors which are different in the group is high. Also, whenthe factors which are present in the groups that are different from eachother are combined, the reliability of prediction is higher thanselecting the factors which are present in the same groups.

Therefore, according to an embodiment, processor 180 repeatedly selectsthe optimal factor combination among a large number of factorcombinations using the plurality of groups, thereby efficientlyminimizing the process of selection by using the combination of thefactors between which the distance is far. Accordingly, it is efficientto preferentially select the combination where the distance is far.

In FIG. 5, for example, a change of values in accordance with acombination of a1 of the group A, b1 of the group B, and c1 of the groupC may be identified whether to correspond to the change of the referencevalues.

According to an embodiment, it is possible to prevent multicollinearitywhich may occur when using a number of factors through grouping andextracting factors.

FIG. 6 illustrates graphs which include time series prediction accordingto an embodiment.

The graph 600 shows actual values and values which are predicted inthree different methods according to an embodiment. A horizontal axis ofthe graph 600 represents time such as, for example, months, while avertical axis represents the actual values and the predicted values ofthe prediction object. It is supposed that the graph 600 illustrates thevalues predicted by various methods in consideration of the actualvalues from January to April. The actual values from January to Octoberare represented as a value line 610. Referring to the predicted valuesother than the actual values, there are illustrated a first predictionline 620 which maintains at an average value of the actual values fromJanuary to April, a second prediction line 630 which is predicted bycomparing the actual values and values of at least one factor relatingto the prediction object, and a third prediction line 640 where a changeof the values is predicted by using the accuracy index in accordancewith an embodiment.

Because the second prediction line 630 analyzes values themselves basedon a difference between the predicted value and the actual value, aratio, etc., the time series change such as an extremum is notconsidered. For example, if the relation between the factors is notconsidered due to predicting a panel price based on a past stock ofpanels, an economy index, etc., a predicted model which has less actualin time series prediction issues may be generated. Also, a predictedresult may be significantly affected, because a factor which is notactually related is selected, for example, a shoe price is considered inmeasuring the panel price.

However, according to an embodiment, the third prediction line 640groups the plurality of factors, and predicts the time series change ofthe factors using various accuracy indexes through combining the factorswhich have the representativeness in each of the groups. Here, becausean optimal factor is adopted by modeling the selected factors intovarious combinations, the value can be actual and more accurate thanpredicting based on a same value or a difference between values.

Referring to the values in FIG. 6 predicted from May to October, thesecond prediction line 630 and the third prediction line 640 of May aremore similar to the actual value of May than the first prediction line620, while the first prediction line 620 of June is more similar to theactual value of June than the second prediction line 630 and the thirdprediction line 640. In this way, the prediction line which has thepredicted value that is mot similar to the actual is different for eachmonth. However, considering in view of time series, it can be understoodthat a pattern of the third prediction line 640 shows the mostsimilarity to that of the value line 610 of the actual value.

That is, according to an embodiment of the disclosure, because theaccuracy index reflects the time series change of the values asdescribed with FIGS. 3 to 5, it can be understood that the changepattern of the values which are calculated based on the accuracy indexhas a similar result to that of the actual values.

FIG. 7 illustrates a flowchart of the electronic apparatus based on theup/down index according to an embodiment.

The up/down index allows for identification of a factor whichcorresponds to the change of the reference values using increase ordecrease of the values of the plurality of factors. The processor 180obtains information related to the values, which occur in time series,of the plurality of factors regarding the prediction object (operationS710), and identifies increase or decrease of change for the values ofthe plurality of factors based on the obtained information (operationS720). For the identification of the increase or decrease of change,processor 180 may use Formulas 1 and 2 used below.

The up/down index uses F1 score which evaluates a categorical predictionsuch as Formula 1 to measure n−1 increases or decreases which occuramong n values that are predicted. After making confusion matrixes orerror matrixes which consider ‘UP’ and ‘DOWN’ as ‘POSITIVE’,respectively, and obtaining two F1 scores using Formula 1, a number ofcases of ‘UP’ and ‘DOWN’ is obtained with a weighted average usingFormula 2.

$\begin{matrix}{\mspace{79mu}{( {F\; 1\mspace{14mu}{score}} ) = \frac{2 \cdot {precision} \cdot {recall}}{{precision} + {recall}}}} & \lbrack {{Formula}\mspace{14mu} 1} \rbrack \\{( {{{Up}/{Down}}\mspace{14mu}{Index}} ) = {( {{{UP}_{tot} \cdot {UP}_{F\; 1}} + {{DOWN}_{tot} \cdot {DOWN}_{F\; 1}}} )/( {{UP}_{tot} + {DOWN}_{tot}} )}} & \lbrack {{Formula}\mspace{14mu} 2} \rbrack\end{matrix}$

The processor 180 identifies the factor which corresponds to the changeof the reference values based on the change that is identified using theabove formulas (operation S730).

According to an embodiment, it is possible to know an interaction effectbetween individual factors, which would be missed if only a relationindex between an individual factor and a corresponding value had beencalculated.

FIG. 8 illustrates a flowchart of the electronic apparatus 100 based onthe extremum index according to an embodiment.

The extremum index uses a point, that is, an extremum at which valueschange in time series from increase to decrease or vice versa. Theextremum includes a maximum point and a minimum point, and is a point atwhich increase or decrease of change happens in time series.

The process to identify an optimal factor based on a change at theextremum is as follows.

The processor 180 identifies a point (referred to as “first point” or“first extremum”) at which values, which occur, change in time seriesfrom increasing to decreasing, or vice versa (operation S810). And, theprocessor 180 identifies another point (referred to as “second point” or“second extremum”) at which the reference values change in time seriesfrom increasing to decreasing, or vice versa (operation S820). Theprocessor 180 identifies at least one factor which corresponds to thetime series change of the reference values based on a correlationbetween the first point and the second point (operation S830). In otherwords, the processor 180 identifies, as the optimal factor, a factorwhich has the first point close to the second point of the referencevalues, that is, a factor which has a pattern of the extremum similar tothat of the reference values.

Additionally, in order to measure the extremum index or predict aninterval during which values changes from increasing to decreasing, orvice versa, a conditional probability such as the following Formula 3may be used.

$\begin{matrix}{( {{Extremum}\mspace{14mu}{Index}} ) = {{\Pr( {{Extremum}\mspace{14mu}{Prediction}\text{|}{Extremum}\mspace{14mu}{Occurrence}} )} = \frac{\begin{matrix}{{Nmber}( {{{m\text{:}\mspace{14mu}{\Delta_{O_{m}} \cdot \Delta_{O_{m + 1}}}} < 0},} } \\ {{{\Delta_{p_{m}} \cdot \Delta_{p_{m + 1}}} < 0},{{\Delta_{O_{m}} \cdot \Delta_{p_{m}}} > 0}} )\end{matrix}}{{Nmber}( {{m\text{:}\mspace{14mu}{\Delta_{O_{m}} \cdot \Delta_{O_{m + 1}}}} > 0} )}}} & \lbrack {{Formula}\mspace{14mu} 3} \rbrack\end{matrix}$

(In Formula 3, p_(m)=predicted value in month m, o_(m)=observed value inmonth m, Δp_(m)=p_(m)−p_(m-1), Δo_(m)=o_(m)−o_(m-1))

Also, the processor 180 may not identify, as the optimal factor, thefactor which has the extremum similar to that of the reference values inconsideration of only the pattern of the extremum but also a number ofthe extrema additionally.

That is, the processor 180 may identify the at least one factor whichcorresponds to the time series change of the reference values based on anumber relation between the first points and the second points usingFormula 3.

Here, such conditional probability may be used because a case in whichtoo many extrema occur in checking the prediction validity of theoperation S450 of FIG. 4 as described above is filtered.

According to an embodiment, in prediction using the time series changein which a trend of the value change of each factor is applied, it ispossible to make the predicted value in accordance with the pattern ofactual time series by minimizing an absolute error between the predictedvalue and the reference value.

FIG. 9 illustrates a flowchart of the electronic apparatus 100 based onthe precedence prediction index according to an embodiment, and FIG. 10illustrates a table used for the precedence prediction index.

The precedence prediction index may be used to check how usefully andaccurately the predicted values forecast the change of the actual valuesat a time preceding an actual point. In the case that the change of thevalues occurs rarely and detecting the pattern in advance is important,it is also important how previously prediction occurs as well as how topredict the values.

Therefore, the processor 180 checks the prediction validity using thefollowing two accuracy variables as the precedence prediction index. Forexample, the accuracy variables include a precedence degree whichindicates how previously from the actual point to predict and anaccuracy which indicates how accurately to predict the change of theactual values.

That is, the processor 180 calculates probability of each factor inaccordance with the precedence degree and the accuracy which arepredicted from the values of each factor (operation S910). Next, theprocessor 180 checks the prediction validity of each factor based on thecalculated probability (operation S920).

The table 1000 of FIG. 10 illustrates performance of a model throughrepeated tests in a test interval using the following Formula 4.

Referring to FIG. 10, the column represents the precedence degree, thatis, a difference as a number of months between the time or point atwhich the prediction is performed and the actual point, whereas the rowrepresents the accuracy, that is, a preceding/following difference as anumber of months between the predicted point and the actual point. Here,the actual point may be a point at which the change of the valuesactually occurs, for example, the extremum, but is not limited thereto.

In the table 1000, how previously and accurately the predicted valuesforecast the change of the actual values may be calculated by theprobability through the following Formula 4.

[Formula 4]

Probability to predict at n months ahead of time previously orsubsequently by

${m\mspace{14mu}{months}} = \frac{X_{mn}}{\sum\limits_{u,v}X_{uv}}$

Precedence prediction error score=Σ_(u,v)w_(uv)*X_(uv),

(In Formula 4, w_(nm)=weight for precedence prediction; value lower as nbecomes larger and m is closer to 0,

X_(nm)=number of months predicted in total test at n months ahead oftime previously or subsequently by m months)

Referring to FIG. 10, X_00, X_01, X_02, and X_03 represent theprobabilities of predicting at times such as, for example, March, April,May, and June, which are the same as or precede an actual change pointof, for example, June, where a predicted point accurately forecasts theactual change point of June. X_10, X_11, X_12, and X_13 represent theprobabilities of predicting at times of, for example, February, March,April, and May, or April, May, June, and July, which are the same as orprecede the actual change point of, for example, May or July within anerror range of one month. X_20, X_21, X_22, and X_23 represent theprobabilities of predicting at times such as, for example, January,February, March and April, or May, June, July, and August, which are thesame as or precede the actual change point of, for example, April orAugust within an error range of two months.

A consistent determination criterion is applied for the precedencedegree or the accuracy, where the more previously or accurate, thehigher the prediction validity. Therefore, the factors which are used incase that the predicted probability of X_03 in FIG. 10 is high meanfactors which have high prediction validity.

Accordingly, the processor 180 checks the prediction validity of eachfactor based on the probability of each factor which is calculated inaccordance with the precedence degree and the accuracy.

It is applicable to set as the index an accuracy after a certain pointin the prediction interval to evaluate the precedence prediction,prediction stability of backtesting, etc., and apply weight or considerpriority in case of a number of indexes being selected.

According to an embodiment, it is possible to calculate the predictedvalue which is more accurate by setting the index adaptively inaccordance with the prediction object and modeling based on factorswhich have high relation to the prediction object.

The electronic apparatus 100 and the control method thereof according toan embodiment may be embodied in a form of a recording medium whichincludes instructions executable by a computer such as a program moduleexecuted by the computer. The computer-readable recording medium mayrefer to an arbitrary usable medium which is accessible by the computer,and includes a volatile or non-volatile medium, a separate or inseparatemedium, etc. Also, the computer-readable recording medium includes acomputer storage medium and a communication medium. The computer storagemedium includes a volatile or non-volatile, separate or inseparatemedium which is embodied in a method or technology for storinginformation such as computer-readable instructions, data structures,program modules, or other data. The communication medium includes atransmission mechanism of computer-readable instructions, datastructures, program modules, or other data of a modulated data signalsuch as a carrier wave, and includes an arbitrary informationtransmission medium.

Although a few embodiments have been shown and described, it will beappreciated by those skilled in the art that changes may be made inthese embodiments without departing from the principles and spirit ofthe present disclosure, the scope of which is defined in the appendedclaims and their equivalents.

What is claimed is:
 1. An electronic apparatus comprising: an interface;and a processor configured to: obtain, via the interface, informationrelated to values, which occur in time series, of a plurality of factorsregarding a prediction object, identify, based on the informationrelated to the values of the plurality of factors, at least one factor,from among the plurality of factors, having a time series change ofvalues that corresponds to a time series change of reference values ofthe prediction object, and output information related to a predictedvalue of the prediction object based on the time series change of thevalues of the at least one factor.
 2. The electronic apparatus accordingto claim 1, wherein the processor is further configured to: identify apoint at which the values of the plurality of factors change in timeseries from increasing to decreasing or change in time series fromdecreasing to increasing, and identify the at least one factor based onthe point.
 3. The electronic apparatus according to claim 2, wherein thepoint is a first point, and wherein the processor is further configuredto identify the at least one factor based on a relation between a numberof first points and a number of second points at which the referencevalues change in time series from increasing to decreasing or change intime series from decreasing to increasing.
 4. The electronic apparatusaccording to claim 1, wherein the processor is further configured to:determine a probability of each of the plurality of factors according toa precedence degree and an accuracy which are predicted from the valuesof the plurality of factors, and determine a prediction validity of eachof the plurality of factors based on the corresponding probability. 5.The electronic apparatus according to claim 1, wherein the processor isfurther configured to: identify an increase or a decrease of the timeseries change of the values of the plurality of factors based on theinformation related to the values of the plurality of factors, andidentify the at least one factor based on the increase or the decreaseof the time series change.
 6. The electronic apparatus according toclaim 1, wherein the processor is further configured to: group theplurality of factors into a plurality of groups which are different fromone another based on a relation to the prediction object, and identifythe at least one factor based on changes of values of the plurality offactors which are included in the plurality of groups.
 7. The electronicapparatus according to claim 6, wherein the processor is furtherconfigured to: determine coordinates for each of the plurality offactors according to the relation, and group factors, from among theplurality of factors, having similar coordinates into a same group. 8.The electronic apparatus according to claim 6, wherein the processor isfurther configured to: identify the at least one factor based on valuesof factors which have representativeness in each of the plurality ofgroups.
 9. The electronic apparatus according to claim 8, wherein theprocessor is further configured to: identify whether a time serieschange of values of a combination of factors, from among the pluralityof factors, which are selected from the plurality of groups, correspondsto the time series change of the reference values.
 10. A method ofcontrolling an electronic apparatus, the method comprising: obtaining,via an interface, information related to values, which occur in timeseries, of a plurality of factors regarding a prediction object,identifying, based on the information related to the values of theplurality of factors, at least one factor, from among the plurality offactors, having a time series change of values that corresponds to atime series change of reference values of the prediction object, andoutputting information related to a predicted value of the predictionobject based on the time series change of the values of the at least onefactor.
 11. The method according to claim 10, wherein the identifyingthe at least one factor comprises: identifying a point at which thevalues of the plurality of factors change in time series from increasingto decreasing or change in time series from decreasing to increasing,and identifying the at least one factor based on the point.
 12. Themethod according to claim 11, wherein the point is a first point, andthe identifying the at least one factor comprises identifying the atleast one factor based on a relation between a number of first pointsand a number of second points at which the reference values change intime series from increasing to decreasing or change in time series fromdecreasing to increasing.
 13. The method according to claim 10, whereinthe identifying the at least one factor comprises: determining aprobability of each of the plurality of factors according to aprecedence degree and an accuracy which are predicted from the values ofthe plurality of factors, and determining a prediction validity of eachof the plurality of factors based on the corresponding probability. 14.The method according to claim 10, wherein the identifying the at leastone factor comprises: identifying an increase or a decrease of the timeseries change of the values of the plurality of factors based on theinformation related to the values of the plurality of factors, andidentifying the at least one factor based on the increase or thedecrease of the time series change.
 15. The method according to claim10, wherein the identifying the at least one factor comprises: groupingthe plurality of factors into a plurality of groups which are differentfrom one another based on a relation to the prediction object, andidentifying the at least one factor based on changes of values of theplurality of factors which are included in the plurality of groups. 16.The method according to claim 15, wherein the grouping the plurality offactors into the plurality of groups comprises: determining coordinatesfor each of the plurality of factors according to the relation, andgrouping factors, from among the plurality of factors, having similarcoordinates into a same group.
 17. The method according to claim 15,wherein the identifying the at least one factor comprises identifyingthe at least one factor based on values of factors which haverepresentativeness in each of the plurality of groups.
 18. The methodaccording to claim 17, wherein the identifying the at least one factorcomprises identifying whether a time series change of values of acombination factors, from among the plurality of factors, which areselected from the plurality of groups, corresponds to the time serieschange of the reference values.
 19. A non-transitory computer-readablemedium storing a computer program including computer-readable code toperform a method of controlling an electronic apparatus, the methodcomprising: obtaining, via an interface, information related to values,which occur in time series, of a plurality of factors regarding aprediction object, identifying, based on the information related to thevalues of the plurality of factors, at least one factor, from among theplurality of factors, having a time series change of values thatcorresponds to a time series change of reference values of theprediction object, and outputting information related to a predictedvalue of the prediction object based on the time series change of thevalues of the at least one factor.